Designer protein assemblies with tunable phase diagrams in living cells


Protein self-organization is a hallmark of biological systems. Although the physicochemical principles governing protein–protein interactions have long been known, the principles by which such nanoscale interactions generate diverse phenotypes of mesoscale assemblies, including phase-separated compartments, remain challenging to characterize. To illuminate such principles, we create a system of two proteins designed to interact and form mesh-like assemblies. We devise a new strategy to map high-resolution phase diagrams in living cells, which provide self-assembly signatures of this system. The structural modularity of the two protein components allows straightforward modification of their molecular properties, enabling us to characterize how interaction affinity impacts the phase diagram and material state of the assemblies in vivo. The phase diagrams and their dependence on interaction affinity were captured by theory and simulations, including out-of-equilibrium effects seen in growing cells. Finally, we find that cotranslational protein binding suffices to recruit a messenger RNA to the designed micron-scale structures.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: A synthetic system for controlled phase separation in living cells.
Fig. 2: Characterization of phase diagrams in living cells.
Fig. 3: Influence of affinity on phase separation in vivo.
Fig. 4: Cotranslational binding of a nascent chain directs mRNA localization.

Data availability

We provide single-cell measurements of YFP and RFP concentrations for all phase diagrams in two Supplementary Excel tables. Other data are available from the authors upon request. Source Data are provided with this paper.

Code availability

Code and custom scripts used in this work are available from the authors upon request. We used the open source package oxDNA (v.2.4) to run the sedimentation simulations.


  1. 1.

    Hyman, A. A., Weber, C. A. & Julicher, F. Liquid–liquid phase separation in biology. Annu. Rev. Cell Dev. Biol. 30, 39–58 (2014).

    CAS  PubMed  Google Scholar 

  2. 2.

    Banani, S. F. et al. Compositional control of phase-separated cellular bodies. Cell 166, 651–663 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Holehouse, A. S. & Pappu, R. V. Functional implications of intracellular phase transitions. Biochemistry 57, 2415–2423 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    CAS  PubMed  Google Scholar 

  5. 5.

    Tatomer, D. C. et al. Concentrating pre-mRNA processing factors in the histone locus body facilitates efficient histone mRNA biogenesis. J. Cell Biol. 213, 557–570 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Buchan, J. R. & Parker, R. Eukaryotic stress granules: the ins and outs of translation. Mol. Cell 36, 932–941 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Su, X. et al. Phase separation of signaling molecules promotes T cell receptor signal transduction. Science 352, 595–599 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Cai, J., Townsend, J. P., Dodson, T. C., Heiney, P. A. & Sweeney, A. M. Eye patches: protein assembly of index-gradient squid lenses. Science 357, 564–569 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Garcia-Seisdedos, H., Empereur-Mot, C., Elad, N. & Levy, E. D. Proteins evolve on the edge of supramolecular self-assembly. Nature 548, 244–247 (2017).

    CAS  PubMed  Google Scholar 

  10. 10.

    Murakami, T. et al. ALS/FTD mutation-induced phase transition of FUS liquid droplets and reversible hydrogels into irreversible hydrogels impairs RNP granule function. Neuron 88, 678–690 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).

    CAS  PubMed  Google Scholar 

  12. 12.

    Peskett, T. R. et al. A liquid to solid phase transition underlying pathological huntingtin exon1 aggregation. Mol. Cell 70, 588–601 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Li, P. et al. Phase transitions in the assembly of multivalent signalling proteins. Nature 483, 336–340 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Bracha, D. et al. Mapping local and global liquid phase behavior in living cells using photo-oligomerizable seeds. Cell 175, 1467–1480 (2018).

    Google Scholar 

  15. 15.

    Flory, P. J. Principles of Polymer Chemistry (Cornell Univ. Press, 1953).

  16. 16.

    Smallenburg, F., Leibler, L. & Sciortino, F. Patchy particle model for vitrimers. Phys. Rev. Lett. 111, 188002 (2013).

    PubMed  Google Scholar 

  17. 17.

    Bianchi, E., Largo, J., Tartaglia, P., Zaccarelli, E. & Sciortino, F. Phase diagram of patchy colloids: towards empty liquids. Phys. Rev. Lett. 97, 168301 (2006).

    PubMed  Google Scholar 

  18. 18.

    Falkenberg, C. V., Blinov, M. L. & Loew, L. M. Pleomorphic ensembles: formation of large clusters composed of weakly interacting multivalent molecules. Biophys. J. 105, 2451–2460 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Jacobs, W. M. & Frenkel, D. Phase transitions in biological systems with many components. Biophys. J. 112, 683–691 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Sanders, D. W. et al. Competing protein-RNA interaction networks control multiphase intracellular organization. Cell 181, 306–324 (2020).

    CAS  PubMed  Google Scholar 

  21. 21.

    Li, W. et al. Dual recognition and the role of specificity-determining residues in colicin E9 DNase–immunity protein interactions. Biochemistry 37, 11771–11779 (1998).

    CAS  PubMed  Google Scholar 

  22. 22.

    Buxbaum, A. R., Haimovich, G. & Singer, R. H. In the right place at the right time: visualizing and understanding mRNA localization. Nat. Rev. Mol. Cell Biol. 16, 95–109 (2015).

    CAS  PubMed  Google Scholar 

  23. 23.

    Isaacs, W. B. & Fulton, A. B. Cotranslational assembly of myosin heavy chain in developing cultured skeletal muscle. Proc. Natl Acad. Sci. USA 84, 6174–6178 (1987).

    CAS  PubMed  Google Scholar 

  24. 24.

    Shiber, A. et al. Cotranslational assembly of protein complexes in eukaryotes revealed by ribosome profiling. Nature 561, 268–272 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Natan, E. et al. Cotranslational protein assembly imposes evolutionary constraints on homomeric proteins. Nat. Struct. Mol. Biol. 25, 279–288 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Kramer, G., Shiber, A. & Bukau, B. Mechanisms of cotranslational maturation of newly synthesized proteins. Annu. Rev. Biochem. 88, 337–364 (2019).

    CAS  PubMed  Google Scholar 

  27. 27.

    Haim-Vilmovsky, L. & Gerst, J. E. m-TAG: a PCR-based genomic integration method to visualize the localization of specific endogenous mRNAs in vivo in yeast. Nat. Protoc. 4, 1274–1284 (2009).

    CAS  PubMed  Google Scholar 

  28. 28.

    Lui, J. et al. Granules harboring translationally active mRNAs provide a platform for P-body formation following stress. Cell Rep. 9, 944–954 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Shin, Y. et al. Spatiotemporal control of intracellular phase transitions using light-activated optoDroplets. Cell 168, 159–171 (2017).

    CAS  PubMed  Google Scholar 

  30. 30.

    Dignon, G. L., Zheng, W., Best, R. B., Kim, Y. C. & Mittal, J. Relation between single-molecule properties and phase behavior of intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 115, 9929–9934 (2018).

    CAS  PubMed  Google Scholar 

  31. 31.

    Dignon, G. L., Zheng, W. & Mittal, J. Simulation methods for liquid–liquid phase separation of disordered proteins. Curr. Opin. Chem. Eng. 23, 92–98 (2019).

    Google Scholar 

  32. 32.

    Yang, P. et al. G3BP1 Is a tunable switch that triggers phase separation to assemble stress granules. Cell 181, 325–345 (2020).

    CAS  PubMed  Google Scholar 

  33. 33.

    Elbaum-Garfinkle, S. et al. The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics. PNAS 112, 7189–7194 (2015).

    CAS  PubMed  Google Scholar 

  34. 34.

    Mackenzie, I. R. et al. TIA1 mutations in amyotrophic lateral sclerosis and frontotemporal dementia promote phase separation and alter stress granule dynamics. Neuron 95, 808–816 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    White, M. R. et al. C9orf72 Poly(PR) dipeptide repeats disturb biomolecular phase separation and disrupt nucleolar function. Mol. Cell 74, 713–728 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Banerjee, P. R., Milin, A. N., Moosa, M. M., Onuchic, P. L. & Deniz, A. A. Reentrant phase transition drives dynamic substructure formation in ribonucleoprotein droplets. Angew. Chem. Int. Ed. Engl. 56, 11354–11359 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Duncan, C. D. S. & Mata, J. Widespread cotranslational formation of protein complexes. PLoS Genet. 7, e1002398 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Shieh, Y.-W. et al. Operon structure and cotranslational subunit association direct protein assembly in bacteria. Science 350, 678–680 (2015).

    CAS  PubMed  Google Scholar 

  39. 39.

    Langdon, E. M. & Gladfelter, A. S. A new lens for RNA localization: liquid–liquid phase separation. Annu. Rev. Microbiol. 72, 255–271 (2018).

    CAS  PubMed  Google Scholar 

  40. 40.

    Boeynaems, S. et al. Protein phase separation: a new phase in cell biology. Trends Cell Biol. 28, 420–435 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Garcia-Seisdedos, H., Villegas, J. A. & Levy, E. D. Infinite assembly of folded proteins in evolution, disease, and engineering. Angew. Chem. Int. Ed. Engl. 58, 5514–5531 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Shen, H. et al. De novo design of self-assembling helical protein filaments. Science 362, 705–709 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Abe, S. et al. Crystal engineering of self-assembled porous protein materials in living cells. ACS Nano 11, 2410–2419 (2017).

    CAS  PubMed  Google Scholar 

  44. 44.

    Reinkemeier, C. D., Girona, G. E. & Lemke, E. A. Designer membraneless organelles enable codon reassignment of selected mRNAs in eukaryotes. Science 363, eaaw2644 (2019).

  45. 45.

    Lee, M. J. et al. Engineered synthetic scaffolds for organizing proteins within the bacterial cytoplasm. Nat. Chem. Biol. 14, 142–147 (2018).

    CAS  PubMed  Google Scholar 

  46. 46.

    Delarue, M. et al. mTORC1 controls phase separation and the biophysical properties of the cytoplasm by tuning crowding. Cell 174, 338–349. (2018).

    CAS  PubMed  Google Scholar 

  47. 47.

    Chavent, M. et al. How nanoscale protein interactions determine the mesoscale dynamic organisation of bacterial outer membrane proteins. Nat. Commun. 9, 2846 (2018).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Alberti, S., Gladfelter, A. & Mittag, T. Considerations and challenges in studying liquid–liquid phase separation and biomolecular condensates. Cell 176, 419–434 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Wang, J. et al. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174, 688–699 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Choi, J.-M., Dar, F. & Pappu, R. V. LASSI: a lattice model for simulating phase transitions of multivalent proteins. PLoS Comput. Biol. 15, e1007028 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Panasenko, O. O. et al. Co-translational assembly of proteasome subunits in NOT1-containing assemblysomes. Nat. Struct. Mol. Biol. 26, 110–120 (2019).

    CAS  PubMed  Google Scholar 

  52. 52.

    Mumberg, D., Müller, R. & Funk, M. Yeast vectors for the controlled expression of heterologous proteins in different genetic backgrounds. Gene 156, 119–122 (1995).

    CAS  PubMed  Google Scholar 

  53. 53.

    Klock, H. E. & Lesley, S. A. The Polymerase Incomplete Primer Extension (PIPE) method applied to high-throughput cloning and site-directed mutagenesis. Methods Mol. Biol. 498, 91–103 (2009).

    CAS  PubMed  Google Scholar 

  54. 54.

    Voth, W. P., Jiang, Y. W. & Stillman, D. J. New ‘marker swap’ plasmids for converting selectable markers on budding yeast gene disruptions and plasmids. Yeast 20, 985–993 (2003).

    CAS  PubMed  Google Scholar 

  55. 55.

    Brachmann, C. B. et al. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 14, 115–132 (1998).

    CAS  PubMed  Google Scholar 

  56. 56.

    Anand, R., Beach, A., Li, K. & Haber, J. Rad51-mediated double-strand break repair and mismatch correction of divergent substrates. Nature 544, 377–380 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Liu, H. et al. CRISPR–ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation. Bioinformatics 31, 3676–3678 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Cohen, Y. & Schuldiner, M. Advanced methods for high-throughput microscopy screening of genetically modified yeast libraries. Methods Mol. Biol. 781, 127–159 (2011).

  59. 59.

    Matalon, O., Steinberg, A., Sass, E., Hausser, J. & Levy, E. D. Reprogramming protein abundance fluctuations in single cells by degradation. Preprint at bioRxiv (2018).

  60. 60.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  Google Scholar 

  61. 61.

    Nandi, S. K., Heidenreich, M., Levy, E. D. & Safran, S. A. Interacting multivalent molecules: affinity and valence impact the extent and symmetry of phase separation. Preprint at (2019).

Download references


We thank J. Gerst and R. R. Nair (Weizmann Institute) for sharing plasmids of the MS2 system, S. Schwartz (Weizmann Institute) for the bRA89 plasmid, F. Sciortino and H. Hofmann for helpful discussions and suggestions and H. Greenblatt for help with computer systems. E.D.L. acknowledges support by the Israel Science Foundation (no. 1452/18), by the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 819318), by the HFSP Career Development Award (award no. CDA00077/2015), by a research grant from A.-M. Boucher and by research grants from the Estelle Funk Foundation, the Estate of Fannie Sherr, the Estate of Albert Delighter, the Merle S. Cahn Foundation, Mrs. Mildred S. Gosden, the Estate of Elizabeth Wachsman and the Arnold Bortman Family Foundation. E.D.L. is an incumbent of the Recanati Career Development Chair of Cancer Research. L.R. acknowledges support from the European Commission (Marie Skłodowska-Curie Fellowship, no. 702298-DELTAS). S.A.S. thanks the BSF and the ISF program and acknowledges the historical generosity of the Perlmann family foundation. S.K.N. acknowledges support from the Koshland foundation and Department of Atomic Energy (DAE), India. E.L. and L.R. thank the Vienna Scientific Cluster (VSC) for computing time.

Author information




M.H., J.M.G. and E.D.L. designed the research and synthetic protein system. M.H. and J.M.G. performed the experiments with help from Y.N. E.L., L.R. and J.K.P.D developed the theoretical framework for modeling the system based on patchy particles. S.K.N. and S.A.S. developed the theoretical framework for modeling the system based on a lattice model. A.S. wrote the image analysis scripts. E.S. carried out electron microscopy experiments. M.H. and E.D.L. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Lorenzo Rovigatti or Samuel A. Safran or Jonathan P. K. Doye or Emmanuel D. Levy.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The components do not form condensates when expressed individually.

Haploid cells expressing only one of the building blocks show a homogenous distribution of fluorescence throughout the cytoplasm. The left-most image shows cells expressing the dimer component lacking the Im2 domain. The next images show cells expressing the variants of the dimer component in the absence of the tetramer component. The right-most image shows cells expressing the tetramer component in the absence of the dimer component. This result was replicated three times.

Extended Data Fig. 2 The synthetic condensates are not membrane-bound.

a, Transmission electron microscopy (TEM) micrograph of fixed and sectioned yeast shows a condensate formed by our minimal system, in the cytoplasm. b, The yellow arrow points to one of several 10 nm gold-labeled anti-GFP antibodies, confirming the identity of the designed compartments. White arrows highlight the lack of membrane surrounding the compartment. c, Scanning electron microscopy micrograph of cells frozen at high-pressure and cryo-fractured reveals the mosaic of amorphic cytoplasm. The region outlined by white carets exhibits a distinct ultrastructure d, Increased magnification of a suspected condensate within the cytoplasm, outlined with white carets. This ultrastructure has no visible membrane. Scale bar 1 µm. We did not carry independent biological replicates of these electron microscopy experiments.

Extended Data Fig. 3 Impact of affinity on the phase diagram of the dimer-tetramer system.

a, We used a lattice model (Supplementary Note, Section 1) of the dimer-tetramer system. In the square lattice, concentration is measured by fractional occupancy of edges and vertices by dimers and tetramers respectively. We calculated the binodal of this system in the plane corresponding to the fractional occupancy of dimer (x-axis) and tetramer (y-axis). Affinity increases in panels from left to right, where μ is the binding energy in units of kT of a linker and one arm of the tetravalent molecule. Higher affinity (larger μ) increases the fraction of the phase-separated region. b, We used mean-field theoretical calculations of patchy particles matching the geometry of the proteins. The binodal is calculated in the plane corresponding to the concentration of dimers (x-axis) and tetramers (y-axis). Affinity (which is linked to the energy and entropy associated with the formation of a bond, see Supplementary Note, Section 1) increases from left to right.

Extended Data Fig. 4 Simulations recapitulate the kinetic trapping effect observed experimentally.

a, Sedimentation molecular dynamics simulation of patchy particles. Several simulations were conducted at equilibrium or out-of-equilibrium while sampling different concentrations of dimer and tetramer. The protein osmotic pressure as a function of density was inferred from each simulation and used to evaluate the phase boundaries. b, The phase diagram of the patchy mixture computed with equilibrium and non-equilibrium simulations (squares and circles, respectively).

Extended Data Fig. 5 In vivo phase diagrams and fluorescence recovery profiles observed with different affinities.

a, In vivo phase diagrams observed for five affinities investigated initially. Concentrations correspond to those of the binding sites (not of the dimer and tetramer complexes). The red line highlights the diagonal, where the concentrations of binding sites of dimer and tetramers are equal. The grey dotted lines show the lower limit of concentrations that can be reliably estimated. b, Fluorescence recovery profiles of photobleached condensates for different interaction affinities between the components. Grey lines show individual experiments, the red line corresponds to the mean recovery and the transparent red area indicates the standard error. The mean recovery after 25 seconds and associated standard error are given for each affinity. Source data

Extended Data Fig. 6 Replicating the measurement of in vivo phase diagrams with four additional affinities.

Phase diagrams measured for nine affinities. Five affinities come from replicating experiments shown in Extended Data Fig. 5, and four are new. Concentrations correspond to those of the binding sites (not of the dimer and tetramer complexes). The red line highlights the diagonal, where the concentrations of binding sites of dimer and tetramers are equal. The grey dotted lines show the lower limit of concentrations that can be reliably estimated. Affinities and mutations are indicated above. The N34V, R38T, double N34V/R38T and triple D33L/N34V/R38T mutants were added later to further investigate the out-of-equilibrium effect. The same number of randomly selected cells were plotted in all panels (n=4000) to allow comparing the density of points across plots. Source data

Extended Data Fig. 7 The mRNA coding for the dimer is released from condensates within minutes after the addition of puromycin.

Cells were treated with a final concentration of 10 mM puromycin and mRNA release from the condensate was followed by time-lapse microscopy.

Supplementary information

Supplementary Information

Supplementary Tables 1–3, Figs. 1–5 and Note.

Reporting Summary

Supplementary Video 1

Synthetic condensates expressed in yeast cells. New synthetic condensates appear in budding daughter cells and their size grows over time. The video is representative of at least three independent experiments.

Supplementary Video 2

FRAP on condensates. Condensates involving high- (left) and low- (right) affinity binding domains show slower (left) and faster (right) recovery after photobleaching. The video is representative of at least 13 independent experiments.

Supplementary Video 3

Localization of dimer mRNA in condensates. mRNAs coding for the dimer building block localize in condensates in yeast cells. The video is representative of at least three independent experiments.

Supplementary Video 4

Localization of GB1 mRNA. mRNAs coding for GB1, a protein that does not bind condensates, colocalize with condensates. The video is representative of at least three independent experiments.

Supplementary Video 5

C-terminal variant of the binding domain. mRNAs do not localize at condensates when the binding domain is encoded at the C terminus of the dimer. The video is representative of at least three independent experiments.

Supplementary Video 6

Puromycin. mRNAs detach from condensates in yeast cells treated with puromycin. The video is representative of at least three independent experiments.

Supplementary Video 7

Puromycin + cyclohexamide. mRNAs remain localized at condensates in yeast cells treated with puromycin and cycloheximide. The video is representative of at least three independent experiments.

Source data

Source Data Figs. 2g and 3a and Extended Data Fig. 5

Data for the phase diagrams.

Source Data Extended Data Fig. 6

Data for the phase diagrams.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Heidenreich, M., Georgeson, J.M., Locatelli, E. et al. Designer protein assemblies with tunable phase diagrams in living cells. Nat Chem Biol 16, 939–945 (2020).

Download citation

Further reading


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing